A genetic algorithm based wrapper feature selection method for classification of hyperspectral images using support vector machine

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Volumn 37, Issue , 2008, Pages 397-402

  Zhuo, Li ^a   Zheng, Jing ^b   Wang, Fang ^a,c   Li, Xia ^a   Ai, Bin ^a   Qian, Junping ^a  

^a SUN YAT SEN UNIVERSITY   (China)

^b Guangdong Climate Centre   (China)

^c GUANGZHOU UNIVERSITY   (China)

Author keywords

Feature Selection; Genetic Algorithm; Hyperspectral; Supported Vector Machine

Indexed keywords

COST REDUCTION; COSTS; FEATURE EXTRACTION; GENETIC ALGORITHMS; IMAGE CLASSIFICATION; OPTIMIZATION; REMOTE SENSING; SET THEORY; SPECTROSCOPY; SUPPORT VECTOR MACHINES; VECTORS;

CLASSIFICATION ACCURACY; CLASSIFICATION PERFORMANCE; FEATURE SELECTION METHODS; HIGH DIMENSIONAL FEATURE; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL; SUPPORTED VECTOR MACHINES; SURVIVAL-OF-THE-FITTEST;

CLASSIFICATION (OF INFORMATION);

EID: 85039915814     PISSN: 16821750     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. Bengio Y., 2000. Gradient-based Optimization of Hyperparameters. Neural Computation, 12(8), pp. 1889-1900.
2. Bi J., Bennett K.P., Embrechts M., Breneman C.M., Song M., 2003. Dimensionality reduction via sparse support vector machines. J. Mach. Learn. Res. 3, pp. 1229-1243.
3. Bradley P. S., Mangasarian O. L., Street W. N., 1998. Feature selection via mathematical programming. INFORMS Journal on Computing, 10, pp. 209-217.
4. Chen M., Yi Y. H., Liu Zh G., Li D. R., QIN Q. Q., 2006. Study of Band Selection Methods of Hyperspectral Image Based on Grouping Property. Bulletin of Surveying and Mapping. 3, pp. 10-13. (In Chinese)
5. Cortes C., Vapnik V.N., 1995. Support vector networks. Mach. Learn., 20(3), pp. 273-297.
6. Cover T.M., 1974. The best two independent measurements are not the two best. IEEE Transactions on Systems, Man, and Cybernetics, 1, pp. 116-117.
7. Elashoff, J.D., Elashoff, R.M., Goldman, G.E., 1967. On the choice of variables In classification problems with dichotomous variables. Biometrika, 54, pp. 668-670.
8. Guyon I., Weston J., Barnhill S., Bapnik V., 2002. Gene selection for cancer classification using support vector machines. Machine Learning, 46(1-3), pp. 389-422.
9. Hsu C. W., Chang C. C., Lin C. J., 2003. A practical guide to support vector classification, http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.
10. Huang Ch. L., Wang Ch J., 2006. A GA-based feature selection and parameters optimization for support vector machines. Expert Systems with Applications, 31, pp. 231-240
11. Keerthi S. S., 2002. Efficient Tuning of SVM Hyperparameters Using Radius/Margin Bound and Iterative Algorithms. IEEE Transactions on Neural Networks, 13(5), pp. 1225-1229.
12. Kohavi R., John G. H., 1997. Wrappers for feature subset selection. Artif. Intell., 97, pp. 273-324.
13. Lee J. H., Lin C. J., 2000. Automatic Model Selection for Support Vector Machines. Taiwan University, Taipei.
14. Liu Y., Zheng Y. F., 2006. FS-SFS: Anovel feature selection method for support vector machines. Pattern Recognition, 39, pp. 1333-1345.
15. Mao K. Z., 2004. Feature subset selection for support vector machines through discriminative function pruning analysis. IEEE Transactions on Systems, Man, and Cybernetics, 34(1), pp. 60-67.
16. Pontil M., Verri A., 1998. Support vector machines for 3D object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(6), pp. 637-646.
17. Raymer M.L., Punch W.F., 2000. Goodman E.D., Kuhn L.A., Jain A.K. Dimensionality reduction using genetic algorithms. IEEE Trans. Evol. Comput., 4(2), pp. 164-171
18. Sikora R., Piramuthu S., 2007. Framework for efficient feature selection in genetic algorithm based data mining. European Journal of Operational Research, 180(2), pp. 723-737.
19. Toussaint, G.T., 1971. Note on optimal selection of independent binary-valued features for pattern recognition. IEEE Transactions on Information Theory IT, 17, pp. 618.
20. Vapnik, V.N., 1995. The Nature of Statistical Learning Theory. Springer-Verlag, New York.
21. Wang C. Y., Liu Zh J., Yan Ch. Y., 2006. A Experimental Study on Imaging Spectrometer Data Feature Selection and Wheat Type Identification. Journal of Remote Sensing. 10(2), pp. 249-255.
22. Weston J., Mukherjee S., Chapelle O., Ponntil M., Poggio T., Vapnik V., 2001. Feature selection for SVMs. Advances in Neural Information Processing Systems, MIT Press, Cambridge, MA.
23. Yang Q. Sh., Li X., 2007. Calibrating urban cellular automata using genetic algorithms. Geographical Research, 26(2), pp. 226-231. (In Chinese)
24. Yang Zh. H., Zhang Y. Zh., Gong D. P., Li Zh. X., Han J. F., 2006. Feature Selection in Hyperspectral Classification Based on Tabu Search Algorithm. Hydrographic Surveying and Charting, 26(4), pp. 11-14. (In Chinese)
25. Yu, E., Cho, S., 2003. GA-SVM wrapper approach for feature selection in keystroke dynamics identity verification. In: Proceedings of 2003 INNS-IEEE International Joint Conference on Neural Networks, pp. 2253-2257.
26. Yu E., Cho S., 2006. Ensemble based on GA wrapper feature selection. Computers & Industrial Engineering, 51(1), pp. 111-116.